Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Neural networks for pattern recognition
Neural networks for pattern recognition
Statistical evaluation of rough set dependency analysis
International Journal of Human-Computer Studies
Fundamenta Informaticae
Uncertainly measures of rough set prediction
Artificial Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Machine Learning
Taming Large Rule Models in Rough Set Approaches
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Case studies: Public domain, multiple mining tasks systems: ROSETTA rough sets
Handbook of data mining and knowledge discovery
Musical Instruments in Random Forest
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Boruta - A System for Feature Selection
Fundamenta Informaticae
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A new method for estimation of attributes’ importance for supervised classification, based on the random forest approach, is presented. Essentially, an iterative scheme is applied, with each step consisting of several runs of the random forest program. Each run is performed on a suitably modified data set: values of each attribute found unimportant at earlier steps are randomly permuted between objects. At each step, apparent importance of an attribute is calculated and the attribute is declared unimportant if its importance is not uniformly better than that of the attributes earlier found unimportant. The procedure is repeated until only attributes scoring better than the randomized ones are retained. Statistical significance of the results so obtained is verified. This method has been applied to 12 data sets of biological origin. The method was shown to be more reliable than that based on standard application of a random forest to assess attributes’ importance.